Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Yang Qianye"'
Publikováno v:
E3S Web of Conferences, Vol 522, p 01047 (2024)
For the problem of large number of target detection algorithm parameters, a lightweight real-time detection algorithm YOLOv5s-MC based on improved YOLOv5s road scenes is proposed. firstly, CA attention is added to the model to improve the sensitivity
Externí odkaz:
https://doaj.org/article/84938d30f7d4487a82a0c176b153be8d
Autor:
Wu, Xiangcen, Wang, Yipei, Yang, Qianye, Thorley, Natasha, Punwani, Shonit, Kasivisvanathan, Veeru, Bonmati, Ester, Hu, Yipeng
Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to
Externí odkaz:
http://arxiv.org/abs/2410.23084
Autor:
Li, Qi, Shen, Ziyi, Yang, Qianye, Barratt, Dean C., Clarkson, Matthew J., Vercauteren, Tom, Hu, Yipeng
Reconstructing 2D freehand Ultrasound (US) frames into 3D space without using a tracker has recently seen advances with deep learning. Predicting good frame-to-frame rigid transformations is often accepted as the learning objective, especially when t
Externí odkaz:
http://arxiv.org/abs/2407.05767
Autor:
Li, Yiwen, Fu, Yunguan, Gayo, Iani J. M. B., Yang, Qianye, Min, Zhe, Saeed, Shaheer U., Yan, Wen, Wang, Yipei, Noble, J. Alison, Emberton, Mark, Clarkson, Matthew J., Barratt, Dean C., Prisacariu, Victor A., Hu, Yipeng
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupe
Externí odkaz:
http://arxiv.org/abs/2402.10728
Autor:
Yi, Weixi, Stavrinides, Vasilis, Baum, Zachary M. C., Yang, Qianye, Barratt, Dean C., Clarkson, Matthew J., Hu, Yipeng, Saeed, Shaheer U.
We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision the segmentation as a boundary detection problem, rather than a pixel-level classification as in previous works. Thi
Externí odkaz:
http://arxiv.org/abs/2308.11376
Autor:
Yan, Wen, Chiu, Bernard, Shen, Ziyi, Yang, Qianye, Syer, Tom, Min, Zhe, Punwani, Shonit, Emberton, Mark, Atkinson, David, Barratt, Dean C., Hu, Yipeng
Publikováno v:
journal={Medical Image Analysis}, volume={91}, pages={103030}, year={2024}, publisher={Elsevier}
One of the distinct characteristics in radiologists' reading of multiparametric prostate MR scans, using reporting systems such as PI-RADS v2.1, is to score individual types of MR modalities, T2-weighted, diffusion-weighted, and dynamic contrast-enha
Externí odkaz:
http://arxiv.org/abs/2307.08279
Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges. One such type of graph is predefined with fixed size and connectivity to represent a refe
Externí odkaz:
http://arxiv.org/abs/2303.06550
Autor:
Saeed, Shaheer U., Syer, Tom, Yan, Wen, Yang, Qianye, Emberton, Mark, Punwani, Shonit, Clarkson, Matthew J., Barratt, Dean C., Hu, Yipeng
We propose an image synthesis mechanism for multi-sequence prostate MR images conditioned on text, to control lesion presence and sequence, as well as to generate paired bi-parametric images conditioned on images e.g. for generating diffusion-weighte
Externí odkaz:
http://arxiv.org/abs/2303.02094
Autor:
Li, Yiwen, Fu, Yunguan, Gayo, Iani, Yang, Qianye, Min, Zhe, Saeed, Shaheer, Yan, Wen, Wang, Yipei, Noble, J. Alison, Emberton, Mark, Clarkson, Matthew J., Huisman, Henkjan, Barratt, Dean, Prisacariu, Victor Adrian, Hu, Yipeng
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and
Externí odkaz:
http://arxiv.org/abs/2209.05160
Autor:
Yang, Qianye, Atkinson, David, Fu, Yunguan, Syer, Tom, Yan, Wen, Punwani, Shonit, Clarkson, Matthew J., Barratt, Dean C., Vercauteren, Tom, Hu, Yipeng
In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an examp
Externí odkaz:
http://arxiv.org/abs/2207.12901